Channel-Aware Multi-Domain Feature Extraction for Automatic Modulation Recognition in MIMO Systems

TL;DR

This paper introduces a channel-aware multi-domain feature extraction framework for automatic modulation recognition in MIMO systems, addressing challenges posed by multi-antenna channels and improving recognition accuracy.

Contribution

It proposes a novel CAMD framework that reconstructs signals with channel compensation and extracts multi-domain features for robust MIMO AMR.

Findings

CAMD outperforms existing methods on MIMOSig-Ref dataset

The framework effectively mitigates multi-antenna channel effects

Experimental results show improved recognition accuracy

Abstract

Automatic modulation recognition (AMR) is a key technology in non-cooperative communication systems, aiming to identify the modulation scheme from signals without prior information. Deep learning (DL)-based methods have gained wide attention due to their excellent performance, but research mainly focuses on single-input single-output (SISO) systems, with limited exploration for multiple-input multiple-output (MIMO) systems. The confounding effects of multi-antenna channels can interfere with the statistical properties of MIMO signals, making identification particularly challenging. To overcome these limitations, we propose a Channel-Aware Multi-Domain feature extraction (CAMD) framework for AMR in MIMO systems. Our CAMD framework reconstructs the transmitted signal through an efficient channel compensation module and achieves a more robust representation capability against channel interference by extracting and integrating multi-domain features, including intra-antenna temporal correlations and inter-antenna channel correlations. We have verified our method on the widely-used dataset, MIMOSig-Ref, with complex mobile channel environments. Extensive experiments confirm the performance advantages of CAMD over previous state-of-the-art methods.